A novel dynamic spatial clustering construction and visualization system is configured to intelligently and autonomously determine optimal office space divisions for improved space efficiency and/or productivity of an organization in a particular office floorplan. Instead of requiring a human operator intervention to input step-by-step instructions or floor plan map manipulations into the system, the dynamic spatial clustering construction and visualization system can autonomously and intelligently determine optimal spatial clustering from a given set of space segments, grouping properties, and geographic features of a computerized office floor plan, by machine-determined isolation and sub-grouping of space marker icons based on known common traits per icon, by determining separate clusters within each subgroup using a density-based clustering algorithm, by creating space marker boundary indicators to approximate required spaces without spatial waste, and by executing a convex hull algorithm on each defined cluster to define the new boundary per cluster without spatial waste.